Use of ASR confidence to improve reliability of automatic audio redaction

ABSTRACT

A speech redaction engine includes a natural language processing (NLP)-based content redaction module receives an automatic speech recognition (ASR) decoding of a decoded portion of said digitized speech signal and utilizes NLP techniques to determine whether it contains sensitive information that should be redacted, and an ASR confidence-based redaction module that receives a confidence indicator and utilizes said confidence indicator to determine, independent of said NLP-based content redaction module, whether said decoded portion contains one or more word(s) that were recognized with a confidence level that is below a threshold. The speech redaction engine includes means for redacting said decoded portion if the NLP-based content redaction module determines that said portion should be redacted, and means for redacting the one or more word(s) if the ASR confidence-based redaction module determines that the one or more word(s) have the confidence level that is below the threshold.

CROSS REFERENCE TO OTHER APPLICATIONS

This application in a Continuation Application and claims the benefit and priority to a co-pending U.S. patent application Ser. No. 16/162,997 entitled USE OF ASR CONFIDENCE TO IMPROVE RELIABILITY OF AUTOMATIC AUDIO REDACTION filed Oct. 17, 2018, which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

This invention relates generally to the fields of audio/speech data processing, audio/speech data archiving, and audio/speech data analytics. More specifically, the invention relates to improved processes/systems for redacting, masking, or anonymizing sensitive data—such as Personally Identifiable Information (PII), Nonpublic Personal Information (NPI), Personal Health Information (PHI), Sensitive Personal Information (SPI), Personal Credit Information (PCI), etc.—from audio/speech recordings.

BACKGROUND OF THE INVENTION

Automatic speech recognition (ASR) is a well-known and widely available technology. State-of-the-art ASR systems are available from companies such as Nuance Communications, Google, and Voci Technologies. There are also several open source ASR packages, such as Sphinx, HTK, and Kaldi. See https://en.wikipedia.org/wiki/List_of_speech_recognition_software#Open_source_acoustin_models_and_speech_corpus_(compilation).

In ASR, “confidence” represents an estimate of the likelihood that the ASR engine has correctly recognized a given word or utterance. Various approaches are known for estimating ASR confidence. The most common approaches involve comparing the relative ranking(s) of the selected decoding versus the non-selected decoding(s). If the selected decoding was ranked far higher by the recognizer than the alternative(s), then the confidence in its correctness is higher; whereas if the selected decoding was not strongly preferred by the recognizer, then the confidence in its correctness is lower.

Other approaches to estimating ASR confidence are known and available in the prior art. See, e.g., H Jiang, “Confidence measures for speech recognition: a survey,” Speech Communication, April 2005 (incorporated by reference herein; copy available at http://www-gtb.upm.es/research/documentation/referenceias/Jang_Confidence.pdf); D J Brems, et al., “Automatic speech recognition (ASR) processing using confidence measures,” U.S. Pat. No. 5,566,272 (incorporated by reference herein); D J Litman, et al., “Predicting automatic speech recognition performance using prosodic cues,” Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference, 2000 (incorporated by reference herein; copy available at https.//aclanthology.info/pdf/A/A00/A00-2029.pdf); P S Huang, et al., “Predicting speech recognition confidence using deep learning with word identity and score features,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 (incorporated by reference herein; copy available at https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ConfidenceEstimator.pdf)

As explained in https://en.wikipedia.org/wiki/Speaker_recognitionn (downloaded Jun. 11, 2018),

-   -   Speaker recognition is the identification of a person from         characteristics of voices (voice biometrics). It is also called         voice recognition. There is a difference between speaker         recognition (recognizing who is speaking) and speech recognition         (recognizing what is being said). These two terms are frequently         confused, and “voice recognition” can be used for both. In         addition, there is a difference between the act of         authentication (commonly referred to as speaker verification or         speaker authentication) and identification. Finally, there is a         difference between speaker recognition (recognizing who is         speaking) and speaker diarization (recognizing when the same         speaker is speaking).     -   Speaker recognition has a history dating back some four decades         and uses the acoustic features of speech that have been found to         differ between individuals. These acoustic patterns reflect both         anatomy (e.g., size and shape of the throat and mouth) and         learned behavioral patterns (e.g., voice pitch, speaking style).         Speaker verification has earned speaker recognition its         classification as a “behavioral biometric”.     -   Speaker recognition is a pattern recognition problem. The         various technologies used to process and store voice prints         include frequency estimation, hidden Markov models, Gaussian         mixture models, pattern matching algorithms, neural networks,         matrix representation, Vector Quantization and decision trees.         Some systems also use “anti-speaker” techniques, such as cohort         models, and world models. Spectral features are predominantly         used in representing speaker characteristics.

Technology currently exists that enables voice-based determination of a caller's demographic profile information. One example is the caller's speaking dialect. See, e.g., F Biadsy, “Automatic Dialect and Accent Recognition and its Application to Speech Recognition,” Ph.D. Thesis, Columbia Univ., 2011 (incorporated by reference herein; copy available at http.//www.cs.columbia.edu/speech/ThesisFiles/fadi_biadsy.pdr); F Biadsy, et al., “Dialect-specific acoustic language modeling and speech recognition,” U.S. Pat. Pub. No. 2015/0287405, Oct. 8, 2015 (incorporated by reference herein); G Talwar, et al., “Speech dialect classification for automatic speech recognition,” U.S. Pat. Pub. No. 2012/0109649, May 3, 2012 (incorporated by reference herein); and G Choueiter, et al., “An empirical study of automatic accent classification,” IEEE ICASSP 2008, pp. 4265-4268 (incorporated by reference herein; copy available at https://groups.csail.mit.edu/sls/publications/2008/ICASSP08_Choueiter_MR.pdf).

Another example is the caller's gender. See, e.g., Y Hu, et al., “Pitch-based Gender Identification with Two-stage Classification,” Security and Communication Networks 5(2):211-225 (2012) (incorporated by reference herein; copy available at http://www.wu.ece.ufl.edu/mypapers/GenderIdentification.pdf); H Printz, et al., “Method and Apparatus for Automatically Determining Speaker Characteristics for Speech-Directed Advertising or Other Enhancement of Speech-Controlled Devices or Services,” U.S. Pat. Pub. No. 2008/0103761 (incorporated by reference herein); E Fokoue, et al., “Speaker Gender Recognition via MFCCs and SVMs” Rochester Inst. of Tech., 2013 (incorporated by reference herein; copy available at http://scholarworks.rit.edu/article/1749).

Yet another example is the caller's (approximate) age. See, e.g., P Nguyen, et al., “Automatic classification of speaker characteristics,” Communications and Electronics (ICCE), 2010 (incorporated by reference herein; copy available at http://fit.hcmup.edu.vn/˜baits/Conferences/ICCE%202010/Full%20Papers/SA/Automatic%20Classification%20of%20Speaker%20Characteristics.pdf); H Meinedo, et al., “Age and Gender Detection in the I-DASH Project,” ACM Trans. on Speech & Lang. Process., August 2011 (incorporated by reference herein; copy available at http.//www.inesc.id.pt/pt/indicadores/Ficheiros/7554.pdf); O Chen, et al., “Method of recognizing gender or age of a speaker according to speech emotion or arousal,” U.S. Pat. No. 9,123,342 (incorporated by reference herein); Y Cao, et al., “Method and apparatus for determining a user age range,” U.S. Pat. No. 9,105,053 (incorporated by reference herein).

The term “sentiment analysis,” as used herein, refers to the use of natural language processing (NLP), text analysis, and/or computational linguistics to identify, extract, quantify, and/or study affective states and subjective information. Sentiment analysis is traditionally applied to voice-of-the-customer (VoC) materials, such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. See, e.g., U.S. Pat. No. 7,136,877, “System and method for determining and controlling the impact of text,” incorporated herein by reference. Most sentiment analysis systems attempt to classify a sentence (or other text grouping) as “positive,” “negative,” or “neutral.” Others may, instead or in addition, assign emotional state labels—such as “angry,” “sad,” or “happy”—to the analyzed text. Numerous open source sentiment analysis packages are available, as described in https://en.wikipedia.org/wiki/Sentiment_analysis (incorporated by reference herein). Sentiment analysis is also available as a cloud-based service. See M Sidana, “Top Five Emotion/Sentiment Analysis APIs for understanding user sentiment trends,” available at https://medium.com/@sifium/top-five-emotional-sentiment-analysis-apis-116cd8d42055.

Technology currently exists for redacting (or scrubbing) sensitive information from unstructured text records. See, e.g., L Hirschman, “Hiding in Plain Sight: De-identification of Clinical Narrative,” MITRE Corp., Oct. 26, 2015 (incorporated herein by reference; copy available at http://projects.iq.harvard.edu/files/researchdataaccessandinnovationsymposium/files/hiding_in_plain_sight_lynettehirschman.pdf); see also “MIST: The MITRE Identification Scrubber Toolkit,” v. 2.04 (incorporated herein by reference; available at http.//mist-deid.sourceforge.net). See also scrubadub, v. 1.2.0 (incorporated herein by reference; available at http://scubadub.readthedocs.io/en/stable/index.html); “De-identifying sensitive data in text content,” Google Cloud (incorporated herein by reference; available at https://cloud.google.com/dlp/docs/deidentify-senstive-data).

An important part of the redaction process involves identification of named entities. See, e.g., Project t3as-redact (“The redaction application uses automatic Named Entity Recognition to highlight names of people, organizations, locations, dates/times/durations and numbers; which are likely targets for redaction.”) (incorporated herein by reference; available at https://github.com/NICTA/t3as-redact). Several named entity recognizers—CoreNLP with Corefs; CoreNLP; OpenNLP; and NICTA NER—are available as part of the t3as-redact package. Id. ASR confidence can be used to improve the recognition of named entities in ASR derived transcripts. See K Sudoh, et al., “Incorporating speech recognition confidence into discriminative named entity recognition of speech data,” ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics (incorporated herein by reference; copy available at http://www.aclweb.org/anthology/P06-1078).

Certain redaction systems permit flexible control over the strictness of the redaction process. See, e.g., D Sanchez, et al., “C-sanitized: a privacy model for document redaction and sanitization,” 2014 (incorporated by reference herein; copy available at https://arxiv.org/pdf/1406.4285. pdf; as parameter “∝” is increased from its nominal value of 1, redactions become stricter—thus reducing the likelihood of inadvertently leaving a sensitive entity “C” in the redacted output) and U.S. Pat. No. 8,938,386, “Classification-based redaction in natural language text” (incorporated by reference herein; “For lower values of μ, more features will be sanitized and one would expect to see greater privacy with some loss of utility.”).

Technology exists to mask the identity of a speaker's voice. See, e.g., J Qian, et al., “VoiceMask: Anonymize and Sanitize Voice Input on Mobile Devices,” Nov. 30, 2017 (incorporated herein by reference; copy available at https://arxiv.org/pdf/1711.11460.pdf), J Qian, et al., “Towards Privacy-Preserving Speech Data Publishing,” IEEE INFOCOM, April 2018 (incorporated herein by reference; copy available at https://www.researchgate.net/publication/322993280_Towards_Privacy-Preserving_Speech_Data_Publishing). See also A Raghunathan, “EVALUATION OF INTELLIGIBILITY AND SPEAKER SIMILARITY OF VOICE TRANSFORMATION,” M. S. Thesis, Univ. of Kentucky, 2011 (incorporated herein by reference; copy available at https://www.vis.uky.edu/˜cheung/doc/thesis-Anusha.pdf); Q Jin, et al., “Speaker de-identification via voice transformation,” MIPRO 2014 (incorporated herein by reference; copy available at https://www.researchgate.net/publication/224096965_Speaker_de-identification_via_voice_transformation); S He, “Speaker De-identification for Privacy Protection,” Loughborough University, September 2016 (incorporated herein by reference; copy available at http://ctoresearch.irdeto.com/materials/shufei_1st_year_report.pdf); C G Mateo, et al., “Voice Conversion Fundamentals for Speaker De-identification Applications,” Universida de Vigo, 2016 (incorporated herein by reference; copy available at http://costic1206.uvigo.es/sites/default/files/TrainingSchool2016/Presentations/Carmen%20Garc%C3%ADa%20Mateo.pdf).

Technology also exists that permits the transformation of accented speech to a normalized (unaccented) state, or to a different accented state. “In contrast with voice conversion, which seeks to transform utterances from a speaker so they sound as if another speaker had produced them, accent conversion seeks to transform only those features of an utterance that contribute to accent while maintaining those that carry the identity of the speaker.” D Felps, et al., “Foreign accent conversion in computer assisted pronunciation training,” Speech Communication 51 (2009), pp. 920-932, at 922 (incorporated by reference herein; copy available at http://research/cse.tamu.edu/prism/publications/sc09_felps.pdf). See also Q Yan, et al., “Analysis by synthesis of acoustic correlates of British, Australian and American accents,” Proc. 2004 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (incorporated by reference herein; copy available at https://www.computer.org/csdl/proceedings/icassp/2004/8484/01/1326066.pdf)

While systems that attempt to automatically redact sensitive information from an audio/speech recording currently exist—see, e.g., U.S. Pat. Pub. 2013/0266127A1, “System and method for removing sensitive data from a recording” and U.S. Pat. No. 9,413,891, “Real-time conversational analytics facility,” both incorporated by reference herein—such existing systems are fundamentally flawed, in that they essentially presume near-100% accuracy for the pre-redaction ASR decoding. Embodiments of the invention are provided that address this flaw.

SUMMARY OF THE INVENTION

As illustrated and claimed hereinbelow, embodiments of this invention utilize ASR confidence estimation to reduce or eliminate the shortcomings of traditional, automatic audio/speech redaction systems.

Accordingly, generally speaking, and without intending to be limiting, one aspect of the invention relates to automatic, computer-implemented process(es) for automatically removing sensitive information from an audio recording, such process(es) including, for example, the following computer-implemented acts: (1) receiving a digitally encoded audio recording; (2) transcoding, if necessary, the audio recording to a pre-defined audio format; (3) using a voice activity detector to identify periods of voice activity and/or inactivity in the audio recording; (4) using an utterance or word separator to segregate the audio recording into a sequence of separated utterances or words; (5) streaming the sequence of separated utterances or words to an ASR server; (6) receiving, for each streamed utterance or word, an associated ASR decoding; (7) receiving, for each ASR decoding, an associated confidence score indicative of a probability that the associated ASR decoding is correct; (8) identifying for redaction: (a) any ASR decoding that contains sensitive information, and (b) any ASR decoding with a confidence score less than a redaction threshold; and (9) preparing a redacted audio recording by eliminating or masking those word(s) and/or utterance(s) for which an associated ASR decoding was identified for redaction.

In some embodiments, receiving an associated confidence score may involve receiving the confidence score from the ASR server, along with the associated ASR decoding. And in some embodiments, receiving an associated confidence score may involve receiving the confidence score from a classifier that is separate from the ASR server.

In some embodiments, the confidence scores may be derived from normalized likelihood scores. In some embodiments, the confidence scores may be computed using an N-best homogeneity analysis. In some embodiments, the confidence scores may be computed using an acoustic stability analysis. In some embodiments, the confidence scores may be computed using a word graph hypothesis density analysis. In some embodiments, the confidence scores may be derived from associated state, phoneme, or word durations. In some embodiments, the confidence scores may be derived from language model (LM) scores or LM back-off behaviors. In some embodiments, the confidence scores may be computed using a posterior probability analysis. In some embodiments, the confidence scores may be computed using a log-likelihood-ratio analysis. In some embodiments, the confidence scores may be computed using a neural net that includes word identity and aggregated words as predictors. And in some embodiments, the confidence scores may be computed using a combination of two or more of the above techniques.

Identifying an ASR decoding that contains sensitive information may involve one or more of: (a) determining whether the ASR decoding contains Personally Identifiable Information (PII); (b) determining whether the ASR decoding contains Nonpublic Personal Information (NPI); (c) determining whether the ASR decoding contains Personal Health Information (PHI); (d) determining whether the ASR decoding contains Sensitive Personal Information (SPI); and/or (e) determining whether the ASR decoding contains Personal Credit Information (PCI).

In some embodiments, identifying an ASR decoding that contains sensitive information includes use of a redaction strictness factor that, depending upon its setting, affects the likelihood of a given ASR decoding being identified as containing sensitive information.

In some embodiments, eliminating or masking those word(s) and/or utterance(s) for which an associated ASR decoding was identified for redaction further involves retaining an encrypted version of the eliminated word(s)/utterance(s).

And in some embodiments, preparing a redacted audio recording further includes de-identifying the voices of speaker(s) in the redacted audio recording.

BRIEF DESCRIPTION OF THE FIGURES

Aspects, features, and advantages of the present invention, and its numerous exemplary embodiments, can be further appreciated with reference to the accompanying set of figures, in which:

FIG. 1 is a high-level block diagram, showing a Front End, Processing Tier, and Storage Tier;

FIG. 2 depicts an exemplary embodiment in which the Front End, Processing Tier, and Storage Tier are all provisioned in one or more Data Processing Cloud(s);

FIG. 3 depicts an exemplary embodiment in which the Processing and Storage Tiers are provisioned in Data Processing Cloud(s), and the Frond End is provisioned On Premises;

FIG. 4 depicts an exemplary embodiment in which the Front End is provisioned in a Data Processing Cloud, and the Processing and Storage Tiers are provisioned On Premises;

FIG. 5 depicts an exemplary embodiment in which the Processing Tier is provisioned in a Data Processing Cloud, and the Front End and Storage Tier are provisioned On Premises;

FIG. 6 depicts an exemplary embodiment in which the Front End and Storage Tier are provisioned in Data Processing Cloud(s), and the Processing Tier is provisioned On Premises;

FIG. 7 depicts an exemplary embodiment in which the Storage Tier is provisioned in a Data Processing Cloud, and the Front End and Processing Tier are provisioned On Premises;

FIG. 8 depicts an exemplary embodiment in which the Front End and Processing Tier are provisioned in Data Processing Cloud(s), and the Storage Tier is provisioned On Premises;

FIG. 9 depicts an exemplary embodiment in which the Front End, Processing Tier, and Storage Tier are provisioned On Premises;

FIG. 10 depicts an exemplary embodiment in which the Front End, Processing Tier, and Storage Tier are provisioned partly in Data Processing Cloud(s) and partly On Premises;

FIG. 11 depicts an exemplary embodiment in which the Front End and Processing Tier are provisioned partly in Data Processing Cloud(s) and partly On Premises, and the Storage Tier is provisioned On Premises;

FIG. 12 depicts an exemplary embodiment in which the Front End and Processing Tier are provisioned partly in Data Processing Cloud(s) and partly On Premises, and the Storage Tier is provisioned in a Data Processing Cloud;

FIG. 13 depicts an exemplary embodiment in which the Front End and Storage Tier are provisioned partly in Data Processing Cloud(s) and partly On Premises, and the Processing Tier is provisioned On Premises;

FIG. 14 depicts an exemplary embodiment in which the Front End and Storage Tier are provisioned partly in Data Processing Cloud(s) and partly On Premises, and the Processing Tier is provisioned in a Data Processing Cloud;

FIG. 15 depicts an exemplary embodiment in which the Processing and Storage Tiers are provisioned partly in Data Processing Cloud(s) and partly On Premises, and the Front End is provisioned On Premises;

FIG. 16 depicts an exemplary embodiment in which the Processing and Storage Tiers are provisioned partly in Data Processing Cloud(s) and partly On Premises, and the Front End is provisioned in a Data Processing Cloud;

FIG. 17 depicts an exemplary embodiment in which the Storage Tier is provisioned partly in a Data Processing Cloud and partly On Premises, and the Front End and Processing Tier are provisioned On Premises;

FIG. 18 depicts an exemplary embodiment in which the Storage Tier is provisioned partly in a Data Processing Cloud and partly On Premises, the Front End is provisioned On Premises, and the Processing Tier is provisioned in a Data Processing Cloud;

FIG. 19 depicts an exemplary embodiment in which the Storage Tier is provisioned partly in a Data Processing Cloud and partly On Premises, the Processing Tier is provisioned On Premises, and the Front End is provisioned in a Data Processing Cloud;

FIG. 20 depicts an exemplary embodiment in which the Storage Tier is provisioned partly in a Data Processing Cloud and partly On Premises, and the Front End and Processing Tier are provisioned in Data Processing Cloud(s);

FIG. 21 depicts an exemplary embodiment in which the Processing Tier is provisioned partly in a Data Processing Cloud and partly On Premises, and the Front End and Storage Tier are provisioned On Premises;

FIG. 22 depicts an exemplary embodiment in which the Processing Tier is provisioned partly in a Data Processing Cloud and partly On Premises, the Front End is provisioned On Premises, and the Storage Tier is provisioned in a Data Processing Cloud;

FIG. 23 depicts an exemplary embodiment in which the Processing Tier is provisioned partly in a Data Processing Cloud and partly On Premises, the Storage Tier is provisioned On Premises, and the Front End is provisioned in a Data Processing Cloud;

FIG. 24 depicts an exemplary embodiment in which the Processing Tier is provisioned partly in a Data Processing Cloud and partly On Premises, and the Front End and Storage Tier are provisioned in Data Processing Cloud(s);

FIG. 25 depicts an exemplary embodiment in which the Front End is provisioned partly in a Data Processing Cloud and partly On Premises, and the Processing and Storage Tiers are provisioned On Premises;

FIG. 26 depicts an exemplary embodiment in which the Front End is provisioned partly in a Data Processing Cloud and partly On Premises, the Processing Tier is provisioned On Premises, and the Storage Tier is provisioned in a Data Processing Cloud;

FIG. 27 depicts an exemplary embodiment in which the Front End is provisioned partly in a Data Processing Cloud and partly On Premises, the Storage Tier is provisioned On Premises, and the Processing Tier is provisioned in a Data Processing Cloud;

FIG. 28 depicts an exemplary embodiment in which the Front End is provisioned partly in a Data Processing Cloud and partly On Premises, and the Processing and Storage Tiers are provisioned in Data Processing Cloud(s);

FIGS. 29-31 show three examples of possible data flows between the Front End, Processing Tier, and Storage Tier, in accordance with the various exemplary embodiments herein;

FIG. 32 depicts a stored call database, in accordance with certain exemplary embodiments herein;

FIG. 33 depicts a block diagram of an ASR engine, in accordance with certain exemplary embodiments herein;

FIG. 34 depicts a block diagram of an audio pre-processor, in accordance with certain exemplary embodiments herein;

FIG. 35 depicts the operation of an audio format converter (or transcoder), in accordance with certain exemplary embodiments herein;

FIG. 36 depicts the operation of a voice activity detector (VAD), in accordance with certain exemplary embodiments herein;

FIG. 37 depicts the operation of a word/utterance separator, in accordance with certain exemplary embodiments herein;

FIG. 38 depicts the operation of an ASR engine, in accordance with certain exemplary embodiments herein;

FIG. 39 depicts the operation of an emotion/sentiment module, in accordance with certain exemplary embodiments herein;

FIG. 40 depicts the operation of a gender identification module, in accordance with certain exemplary embodiments herein;

FIG. 41 depicts the operation of an age identification module, in accordance with certain exemplary embodiments herein;

FIG. 42 depicts the operation of a speaker identification module, in accordance with certain exemplary embodiments herein;

FIG. 43 depicts the operation of an accent identification module, in accordance with certain exemplary embodiments herein;

FIG. 44 depicts a structured data record, in accordance with certain exemplary embodiments herein;

FIG. 45 contains a block diagram of a speech redaction engine, in accordance with certain exemplary embodiments herein;

FIG. 46 depicts a first enhanced redaction flow, in accordance with one of the exemplary embodiments herein;

FIG. 47 depicts a second enhanced redaction flow, in accordance with another of the exemplary embodiments herein;

FIG. 48 depicts a third enhanced redaction flow, in accordance with another of the exemplary embodiments herein;

FIG. 49 depicts a first exemplary assignment of the enhanced redaction flow elements (of FIGS. 46-48 ) between the Front End, Processing, and Storage Tiers, in accordance with certain exemplary embodiments herein;

FIG. 50 depicts a second exemplary assignment of the enhanced redaction flow elements between the Front End, Processing, and Storage Tiers, in accordance with certain exemplary embodiments herein;

FIG. 51 depicts a third exemplary assignment of the enhanced redaction flow elements between the Front End, Processing, and Storage Tiers, in accordance with certain exemplary embodiments herein;

FIG. 52 depicts a fourth exemplary assignment of the enhanced redaction flow elements between the Front End, Processing, and Storage Tiers, in accordance with certain exemplary embodiments herein;

FIG. 53 depicts a fifth exemplary assignment of the enhanced redaction flow elements between the Front End, Processing, and Storage Tiers, in accordance with certain exemplary embodiments herein; and,

FIG. 54 depicts a sixth exemplary assignment of the enhanced redaction flow elements between the Front End, Processing, and Storage Tiers, in accordance with certain exemplary embodiments herein.

FURTHER DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Referring to FIGS. 10-28 , the depicted data link(s) are each preferably provisioned as one or more secure data tunnels, using, for example, a secure shell (SSH) protocol. See e protocol (incorporated by reference herein). Indeed, such SSH-provisioned data link(s) may be used to support any, or all, of the data communication links between functional or structural components of the various embodiments herein.

Reference is now made to FIGS. 29-31 , which show three examples of possible data flows between the Front End, Processing Tier, and Storage Tier, in accordance with the various exemplary embodiments herein. While the arrows depict the directionality of “data” transfers (such as audio data, text, meta-data, etc.), it will be understood that there will or may exist additional signaling and control flows that may simultaneously operate in other directions or between other components. For example, in FIG. 29 , data flow from the Front End to the Storage Tier is indicated as one way; however, those skilled in the art will appreciate that there will likely be some signaling channel or pathway that, for example, permits the Storage Tier to signal its readiness to receive data to the Storage Tier.

FIG. 29 depicts an exemplary embodiment in which data is collected by or originates in the Front End, is then “sent” or “routed”—by, for example, a push protocol (see, e.g., https://en.wikipedia.org/wiki/Push_technology, incorporated by reference herein) or a pull/get protocol (see, e.g., https://en.wikipedia.org/wiki/Pull_technology, incorporated by reference herein)—to the Storage Tier. Data is then sent from the Storage Tier to the Processing Tier for processing, with the processed data sent back to the Storage Tier for storage/archiving. This embodiment also permits data that already exists in the Storage Tier, or is sent there through other network connections, to be routed to the Processing Tier for processing and sent back for storage/archiving.

FIG. 30 depicts an exemplary embodiment in which data is collected by or originates in the Front End, is then sent directly to the Processing Tier for processing, and then sent to the Storage Tier for storage/archiving. Such direct data transfer from the Front End to the Processing Tier reduces latency, which is important in the case of systems that have “real time” monitoring or alerting aspects. This embodiment also permits data that already exists in the Storage Tier, or is sent there through other network connections, to be routed to the Processing Tier for processing and sent back for storage/archiving. Additionally, though not depicted in FIG. 30 , “real time” systems may interact directly with the Processing Tier to receive processed data without the additional latency associated with the Storage Tier.

FIG. 31 depicts a “hybrid” embodiment, in which data is collected by or originates in the Front End, some or all of which may be then sent directly to the Processing Tier for processing, then sent to the Storage Tier for storage/archiving, and some or all of which may also be sent to the Storage Tier, from which it is then sent to the Processing Tier for processing, with the processed data sent back to the Storage Tier for storage/archiving. This permits use of the direct data routing approach for “real time” audio feeds, and lower cost “batch mode” processing for other data feeds, which can be processed during time(s) when power and cloud resources are cheaper, for example.

Referring now to FIGS. 46-48 , which depict exemplary flow diagrams for improved automatic redaction flows that employ ASR confidence to avoid or minimize the effect of poor recognition on redaction completeness, additional detail regarding the depicted elements is as follows:

Receive audio: Audio may be received or obtained from any source, whether a “live” feed (such as CTI, VOIP tap, PBX) or a recorded source (such as on-prem storage, cloud storage, or a combination thereof). Thus, the depicted Source Audio may be a disk, or a provisioned portion of a data processing cloud (configured as a cloud PBX), for example.

Transcode: Audio transcoding is the direct digital-to-digital conversion of one audio encoding format to another (e.g., MP3 to WAV). This is an optional step. Typically, all audio is transcoded to a single, least-compressed format (such as WAV) prior to ingestion into an ASR engine. Numerous audio transcoders are available free of charge. See https://en.wikipedia.org/wiki/List_of_audio_conversion_software (incorporated by reference herein).

VAD: Voice activity detection is another optional step. Its main function is to eliminate dead space, to improve utilization efficiency of more compute-intensive resources, such as the ASR engine, or of storage resources. VAD algorithms are well known in the art. See https://en.wikipedia.org/wiki/Voice_activity_detection (incorporated by reference herein).

Segregate: Segregation of the speech input into words or utterances (preferred) is performed as an initial step to ASR decoding. Though depicted as a distinct step, it may be performed as part of the VAD or ASR processes.

Confidence: Confidence may be determined either by the ASR engine (as per FIG. 46 ) or using a separate confidence classifier (as per FIGS. 47 and 48 ). As shown in FIG. 47 , the confidence classifier may operate from the same input stream as the ASR, or may utilize both the input and output of the ASR in its computation, as per FIG. 48 .

Sensitive information: Identifying sensitive information may utilize simple text queries (such as simple number searches) or more sophisticated NLP and/or language classification techniques. As previously discussed, numerous techniques are known and available for identifying redaction targets in unstructured text.

Low ASR confidence: If ASR confidence dips below a “threshold” value, then a word, phrase, or utterance that would ordinarily be passed unredacted will nevertheless be flagged for redaction. In some embodiments, the “threshold” is preset; whereas in other embodiments, it may vary dynamically, based for example on the moving average of confidence values being seen by the redaction module.

Prepare redacted audio: This process involves removing (or masking with noise or voice-over token, e.g., “name”, “health condition”) those portions of the source audio that have been flagged for redaction. Preparing redacted audio preferably involves the creating of a structured data record (e.g., FIG. 44 ) that would include associated meta-data, as well as unredacted records encoded with appropriate levels of encryption. In some embodiments, additional processing of the source audio may be employed to eliminate all personally identifiable characteristics by, for example, using voice de-identification processing and/or accent normalization, as previously discussed.

Redacted audio: The redacted audio may be stored in any means of permanent or temporary data storage (e.g., disk or cloud), or may be immediately passed on for ingestion into an analytics or real-time alerting system.

FIGS. 49-54 depict how these functions/modules can be assigned amongst the Front End, Processing Tier, and Storage Tier, in accordance with the invention herein. These, however, are merely illustrative, and not meant to be in any way limiting. Furthermore, FIGS. 2-28 illustrate how the Front End, Processing Tier, and Storage Tier functions may be provisioned in cloud(s), on premises, or in embodiments that bridge the on-prem/cloud boundary with secure data links. 

The invention claimed is:
 1. A method comprising: receiving an automatic speech recognition (ASR) decoding of a decoded portion of a digitized speech signal; determine determining whether said decoded portion of said digitized speech signal contains sensitive information that should be redacted by utilizing a national language processing (NLP); receiving a confidence score and utilizing said confidence score to determine, independent of said NLP-based content redaction determination, whether said decoded portion of said digitized speech signal contains one or more word(s) within said digitized speech signal that were recognized with a confidence level that is below a threshold; redacting said decoded portion of said digitized speech signal if the NLP-based content redaction determination determines that said portion should be redacted; and redacting the one or more word(s) within said digitized speech signal if the ASR confidence-based redaction module determines that the one or more word(s) have the confidence level that is below the threshold.
 2. The method of claim 1 further comprising means for encrypting and storing an encrypted version of said digitized speech signal prior to redaction.
 3. The method of claim 1 further comprising storing a redacted version of said digitized speech signal and an encrypted version of the decoded portion of said digitized speech signal that is unredacted.
 4. The method of claim 1, wherein the ASR confidence score is derived, at least in part, from normalized likelihood scores.
 5. The method of claim 1, wherein the ASR confidence score is computed, at least in part, using an N-best homogeneity analysis.
 6. The method of claim 1, wherein the ASR confidence score is computed, at least in part, based an acoustic stability analysis.
 7. The method of claim 1, wherein the ASR confidence score is computed, at least in part, based on a word graph hypothesis density analysis.
 8. The method of claim 1, wherein the ASR confidence score is derived, at least in part, based on state, phoneme, or word durations.
 9. The method of claim 1, wherein the ASR confidence score is derived, at least in part, from language model (LM) scores or LM back-off behaviors.
 10. The method of claim 1, wherein the ASR confidence score is computed, at least in part, using a posterior probability analysis.
 11. The method of claim 1, wherein the ASR confidence score is computed, at least in part, using a log-likelihood-ratio analysis.
 12. The method of claim 1, wherein the ASR confidence score is computed, at least in part, using a neural net that includes word identity and aggregated words as predictors.
 13. The method of claim 1, wherein identifying whether the decoded portion contains sensitive information is based, at least, on Personally Identifiable Information (PII).
 14. The method of claim 1, wherein identifying whether the decoded portion contains sensitive information is based, at least, on Nonpublic Personal information (NPI).
 15. The method of claim 1, wherein identifying whether the decoded portion contains sensitive information is based, at least, on Personal Health Information (PHI).
 16. The method of claim 1, wherein identifying whether the decoded portion contains sensitive information is based, at least, on Sensitive Personal Information (SPI) or Personal Credit Information (PCI).
 17. The method of claim 1 further comprising de-identifying a voice of a speaker in the digitized speech signal.
 18. The method of claim 1 further comprising normalizing accent associated with the digitized speech signal.
 19. The method of claim 1 further comprising de-identifying the redacted digitized speech signal.
 20. The method of claim 1, wherein said digitized speech signal is determined to contain sensitive information based on a redaction strictness factor that, depending upon its setting, affects the likelihood of a given ASR decoding being identified as containing sensitive information. 